Front. Oncol.Frontiers in OncologyFront. Oncol.2234-943XFrontiers Media S.A.10.3389/fonc.2025.1737182Original ResearchPrediction of non-small cell lung cancer subtypes is possible through restricted spectrum imagingShenLei1*†Writing – original draftMethodologyZhangYipin2†Writing – original draftMethodologyHuangZhun1†SoftwareWriting – original draftDaiBo1SoftwareWriting – review & editingYangYang3Writing – review & editingInvestigationWangZhe4Writing – review & editingSoftwareValidationYuXuan1Writing – review & editingMethodologyMengNan1Writing – review & editingFormal analysisFuFang Fang1*Writing – review & editingDepartment of Radiology, Henan Provincial People’s Hospital and Zhengzhou University People’s Hospital, Zhengzhou, ChinaNorth Henan Medical University, Xinxiang, ChinaBeijing United Imaging Healthcare Co., Ltd., Beijing, ChinaCentral Research Institute, United Imaging Healthcare Group, Shanghai, ChinaCorrespondence: Fang Fang Fu, fufangf@126.com; Lei Shen, shenlei0502@163.com
These authors have contributed equally to this work and share first authorship
To evaluate the utility of restricted spectrum imaging (RSI) for predicting subtypes of non-small cell lung cancer (NSCLC).
Methods
A total of 97 patients with NSCLC (30 with squamous cell carcinoma (SCC) and 67 with adenocarcinoma (AC)) were included. The parameters f1, f2, f3, apparent diffusion coefficient (ADC), and maximum standardized uptake value (SUVmax) were measured and compared between the two subtypes. Logistic regression analysis was used to identify independent predictors, and a combined diagnostic model was developed. The performance of the model was assessed using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).
Results
Compared with the AC group, the SCC group exhibited significantly higher SUVmax, f2, and f3 values, and lower ADC and f1 values (all P < 0.05). Smoking status, f1, SUVmax, and ADC were independent predictors of NSCLC subtypes. The combined model demonstrated superior diagnostic accuracy (AUC = 0.909; sensitivity = 73.33%; specificity = 89.55%) compared with individual predictors (AUC = 0.693, 0.819, 0.767, and 0.742 for smoking status, f1, SUVmax, and ADC, respectively; all P < 0.01). Bootstrap resampling (1000 samples) validated the robustness of the model (AUC = 0.895). Calibration curves and DCA confirmed the model’s stability and clinical utility.
Conclusion
RSI can effectively differentiate NSCLC subtypes.
diffusion-weighted imagingnon-small cell lung cancerquantitative imagingrestricted spectrum imagingsubtypesThe author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Natural Science Foundation of Henan Province (252300420565), the Science and Technology Project of Henan Province (252102311105), and the Key Project of Henan Province Medical Science and Technology Project (LHGJ20240036, LHGJ20240053).section-at-acceptanceCancer Imaging and Image-directed InterventionsIntroduction
Non-small cell lung cancer (NSCLC) is one of the most fatal malignancies globally (1). Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are both prevalent histological subtypes of NSCLC, but their diagnosis and prognosis differ considerably (2). For example, in terms of surgical procedures, SCC may require more extensive airway reconstruction, whereas AC surgery is less invasive; in terms of drug therapy, patients with AC are more suitable for targeted therapy, while those with SCC are more suitable for immunotherapy. Additionally, SCC and AC differ in the assessment of recurrence risk and drug resistance (3–5). Therefore, accurate assessment of NSCLC subtypes before treatment is of great significance for the development of personalized treatment plans in clinical practice.
Although image-guided biopsy and bronchoscopy remain the gold standards for NSCLC subtype identification, they are invasive and pose risks such as bleeding and pneumothorax (6). Advances in quantitative imaging techniques have provided noninvasive alternatives for tumor characterization. Diffusion-weighted imaging (DWI), which assesses the diffusion of water molecules within tissues, and ¹18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET), which evaluates tumor metabolism, are widely used for lung cancer assessment (7, 8). Restricted spectrum imaging (RSI) is an advanced diffusion MRI model that improves on conventional DWI by distinguishing water diffusion into restricted, hindered, and free compartments through a linear combination of diffusion-weighted models (9, 10). This enables RSI to quantitatively characterize the movement of water molecules in biological tissues with greater precision. RSI has been preliminarily applied in tumor evaluation. For instance, a study by Krishnan et al. showed that RSI helped improve the risk stratification of patients with glioblastoma (11); a study by Zhang et al. found that RSI-derived metrics could be used to noninvasively and effectively identify microvascular invasion in hepatocellular carcinoma (12); and a breast-related study conducted by He et al. concluded that RSI was able to quantitatively characterize breast lesions and normal fibroglandular tissue (13). However, in lung cancer research, to the best of our knowledge, only a few studies have explored the value of RSI in identifying benign and malignant lesions (14).
This study aims to evaluate the diagnostic value of RSI-derived quantitative parameters in differentiating SCC from AC, compare these parameters with classical 18F-FDG PET and DWI metrics, and combine them to develop a diagnostic tool. The ultimate goal is to provide a novel reference for the noninvasive assessment of NSCLC subtypes.
Materials and methodsPopulation
This research was approved by the local ethics review board, and all participants provided written informed consent. From June 2021 to October 2025, a total of 142 patients suspected of having lung cancer based on clinical evaluation or CT imaging underwent chest multiparametric scanning. The exclusion criteria were as follows: 1) Patients with claustrophobia or other conditions that prevented the completion of all imaging sequences (n = 8); 2) Scans with poor image quality (e.g., significant artifacts) that made them unsuitable for analysis (n = 14); 3) Cases where the interval between scanning and biopsy exceeded two weeks (n = 10); 4) Histological findings that did not indicate SCC or AC (n = 7); and 5) Patients who had received relevant treatment before scanning (n = 6). After applying these criteria, 97 patients were included in the study. Baseline characteristics such as age, sex, smoking status, and tumor size were recorded.
Scanning protocols
The MRI sequences (3.0 T system, uPMR790, United Imaging, Shanghai, China) included axial T2-weighted imaging (T2WI) and DWI with multiple b-values. The 18F-FDG used in this study was sourced from FracerLab FX-FDG (GE Minitrac), with a purity > 95% and a pH range of 4.5–8.5. Patients fasted for at least 6 hours to ensure their serum glucose levels remained ≤ 6.5 mmol/L before receiving an 18F-FDG injection (0.11 mCi/kg). The PET scan began 60 minutes after injection, covering the upper thoracic inlet to the lower lung margin with the patient in the supine position (15, 16). PET image reconstruction was performed using the ordered subset expectation maximization (OSEM) method (2 iterations, 20 subsets, voxel size 2.6 × 2.6 × 2.0 mm³). A detailed summary of the protocol specifications is provided in Table 1.
T2WI, T2-weighted imaging; DWI, diffusion-weighted imaging; FSE, fast spin echo; SS-EPI, single shot echo planar imaging; TR/TE, repetition time/echo time; FOV, field of view.
Parameter generation
The acquired images were transferred to a post-processing workstation (uWS-MR005, United Imaging, Shanghai, China) for registration, motion correction, and in-depth analysis. DWI and RSI data were processed using diffusion analysis software from the Advanced Analysis Toolkit. The DWI parametric pseudo-color map was generated using Equation 1:
Sb/S0=exp(− b×ADC)
where ADC represents the apparent diffusion coefficient, b is the diffusion sensitizing factor, and S0 and Sb denote the signal intensities (SIs) at b = 0 s/mm² and b = [specified value] s/mm², respectively (8). The RSI parametric pseudo-color map was constructed using Equation 2:
S(b)=f1e−bD1+f2e−bD2+f3e−bD3,D1<D2<D3
where f1, f2, and f3 represent the volume fractions of the restricted, hindered, and free water diffusion compartments, respectively, and D1, D2, and D3 denote the ADCs of these compartments. To prevent overfitting, ensure model linearization, and maintain comparability across compartments, D1, D2, and D3 were globally assigned values of 1.0 × 10-3 mm²/s, 2.0 × 10-3 mm²/s, and 3.0 × 10-3 mm²/s, respectively, based on established theoretical values and experimental data (17, 18).
Tumor margins on axial T2-weighted images were manually delineated slice by slice to define regions of interest (ROIs). Lesions with cystic degeneration, necrosis, hemorrhagic artifacts, or blood vessels were excluded. The finalized ROIs were then mapped onto pseudo-color DWI and RSI parameter maps, and the mean values were extracted. The volume of interest (VOI) was automatically delineated, and the maximum standardized uptake value (SUVmax) was calculated using PET fusion software. Two radiologists performed these procedures independently: an attending radiologist with 8 years of experience and an associate chief radiologist with 15 years of experience. Both were blinded to each other’s results and the patients’ clinicopathological details.
Histopathologic assessment
Tumor specimens were obtained through surgical resection or biopsy. Histological subtype classification was performed in accordance with the guidelines of the International Association for the Study of Lung Cancer (IASLC) (19).
Data analysis
We employed R (version 3.5.3, R Foundation, Auckland, New Zealand) and SPSS (version 27.0, MedCalc Software, Ostend, Belgium) to conduct data analysis. To assess the interobserver agreement for the parameters, we utilized the interclass correlation coefficient (ICC). An ICC > 0.75 was considered indicative of satisfactory reliability (20). Based on the characteristics of the variables, different statistical tests were applied to compare data between the SCC and AC groups. These tests included the Mann–Whitney U test, independent samples t - test and chi - square test.
R (version 3.5.3, R Foundation, Auckland, New Zealand) and SPSS (version 27.0, IBM Corp., Armonk, NY, USA) were used for data analysis. The interclass correlation coefficient (ICC) was employed to assess the interobserver agreement for the parameters. An ICC > 0.75 was considered indicative of satisfactory reliability (20). Based on the characteristics of the variables, different statistical tests were applied to compare data between the SCC and AC groups, including the Mann–Whitney U test, independent samples t-test, and chi-square test. The diagnostic performance of the parameters was assessed using the area under the receiver operating characteristic curve (AUC). The DeLong test was used to compare differences in AUC values. Logistic regression (LR) analysis was performed to identify independent predictors and develop a multiparameter composite diagnostic tool. Bootstrap resampling (1000 samples), calibration curves, and decision curve analysis (DCA) were used for internal validation and evaluation of the diagnostic tool. Statistical significance was set at P < 0.05.
ResultsBaseline characteristics
A total of 30 patients with SCC and 67 patients with AC were included. Significant differences were observed between the two groups in maximum lesion diameter (P < 0.001), smoking status (P < 0.001), and sex distribution (P = 0.022). However, there was no significant difference in age between the two groups (P = 0.614). The clinical characteristics are summarized in Figure 1, Table 2.
A male patient with adenocarcinoma of the upper lobe of the right lung (arrowheads, maximum diameter = 5.5cm, no smoking). (a) Map of T2-weighted imaging; (b) Map of DWI (b = 600 s/mm2); (c) Map of 18F-FDG PET; (d) Pseudo colored map of ADC; (e) Pseudo colored map of f1; (f) Pseudo colored map of f2; (g) Pseudo colored map of f3; (h) Pathological images (original magnification, ×100).
Medical imaging series showing a breast lesion from different MRI techniques and a histopathological image. Panels (a) and (b) depict traditional MRI scans with a highlighted lesion. Panels (c) to (g) display advanced MRI images with various color maps indicating the lesion's properties. Panel (h) provides a microscopic view of the tissue, showing detailed cellular structures. Each panel includes an arrow pointing to the lesion.
Comparison of various variables between SCC and AC groups.
Variables
SCC (n = 30)
AC (n = 67)
t/χ2/z value
P-value
Age (years)
62.47 ± 8.85
61.51 ± 8.01
0.508
0.614a
Sex distributionn (%)
5.235
0.022b
Female
5 (16.67%)
27 (40.30%)
Male
25 (83.33%)
40 (59.70%)
Maximum lesion diameter (cm)
3.45 (2.38, 4.50)
2.00 (1.50, 3.00)
-3.382
< 0.001c
Smoking status n (%)
21/30 (70.00%)
21/67 (31.34%)
12.613
< 0.001b
f1 (×10−2)
67.11 ± 17.27
86.27 ± 13.01
-5.426
< 0.001a
f2 (×10−2)
8.71 (1.64, 12.93)
0.24 (0.00, 4.33)
-4.278
< 0.001c
f3 (×10−2)
21.39 (11.88, 33.68)
8.32 (0.01, 19.41)
-4.402
< 0.001c
ADC (×10−3mm2/s)
1.11 ± 0.35
1.37 ± 0.25
-3.600
< 0.001a
SUVmax (g/cm3)
9.93 (5.30, 12.80)
3.77 (1.88, 6.64)
-4.184
< 0.001c
AC, Adenocarcinoma; SCC, Squamous Cell Carcinoma.
Independent t-test.
Chi-squared test.
Mann–Whitney U test.
ICC test
Measurements of f1, f2, f3, ADC, and SUVmax showed excellent interobserver agreement, with all ICC values > 0.80. Therefore, the average values from both readers were used for subsequent analysis.
Parameter comparison
The SCC group exhibited significantly higher SUVmax, f2 and f3 values compared to the AC group, while ADC and f1 values were lower (all P < 0.05, Figure 2, Table 2).
Comparison of (a) maximum lesion diameter, (b) ADC, (c) SUVmax, (d) f1, (e) f2 and (g) f3 between squamous cell carcinoma (SCC) and adenocarcinoma (AC) groups.
Bar graphs labeled a to f compare measurements between SCC and AC categories with significant differences noted by p < 0.001. Graph (a) shows maximum diameter, (b) shows ADC values, (c) shows SUVmax values, (d) shows f1, (e) shows f2, and (f) shows f3. Each graph displays variations in values for SCC (orange) and AC (blue).<italic>LR</italic> analysis
Univariate analysis identified sex distribution, smoking status, maximum lesion diameter, f1, f2, f3, ADC, and SUVmax as significant predictors for differentiating SCC from AC (all P < 0.05). Multivariate analysis identified smoking status, f1, ADC, and SUVmax as independent predictors of differentiation, with corresponding P-values of 0.024, 0.001, 0.033, and 0.018, respectively (Table 3).
Univariate and multivariate analyses.
Parameters
Univariate analyses
Multivariate analyses
OR (95% CI) P-value
OR (95% CI) P-value
Age (year)
1.014 (0.962 ~ 1.069)
0.595
/
/
Female
7.407 (2.523 ~ 21.750)
< 0.001
/
/
Maximum lesion diameter (cm)
1.709 (1.213 ~ 2.409)
0.002
/
/
Smoking
5.111 (2.004 ~ 13.003)
< 0.001
4.266 (1.205 ~ 15.012)
0.024
f1 (×10−2)
0.920 (0.886 ~ 0.955)
< 0.001
0.918 (0.872 ~ 0.967)
0.001
f2 (×10−2)
1.171 (1.078 ~ 1.272)
< 0.001
/
/
f3 (×10−2)
1.094 (1.047 ~ 1.143)
< 0.001
/
/
ADC (×10−3mm2/s)
0.030 (0.004 ~ 0.241)
< 0.001
0.045 (0.003 ~ 0.781)
0.033
SUVmax (g/cm3)
1.258 (1.119 ~ 1.414)
< 0.001
1.193 (1.031 ~ 1.380)
0.018
All factors with P < 0.05 in univariate analyses were included in multivariate regression analyses. OR, odds ratio; CI, confidence interval.
Diagnostic performance
The composite model of independent predictors achieved the best diagnostic performance, with an AUC of 0.909, a sensitivity of 73.33%, and a specificity of 89.55%. This performance was significantly superior to that of individual parameters (sex, smoking status, maximum lesion diameter, f1, f2, f3, ADC, and SUVmax; AUCs = 0.715, 0.693, 0.715, 0.819, 0.769, 0.780, 0.742, and 0.767; Z = 4.175, 4.252, 3.525, 2.252, 2.994, 2.758, 3.194, and 2.960; P < 0.001, < 0.001, < 0.001, = 0.024, = 0.003, = 0.006, = 0.001, and = 0.003, respectively) (Figure 3, Table 4).
The areas under receiver-operator characteristic (ROC) curves: (a) includes lines for f1, f2, f3, ADC, and Combined Diagnosis (smoking + f1 + SUVmax + ADC); (b) includes lines for SUVmax, Maximum Diameter, Female, Smoking, and Combined Diagnosis (smoking + f1 + SUVmax + ADC).
Two ROC curve graphs comparing sensitivity and 1-specificity. Graph (a) includes lines for f1, f2, f3, ADC, and Combined Diagnosis. Graph (b) includes lines for SUVmax, Maximum Diameter, Female, Smoking, and Combined Diagnosis. Each line style and color represents a different parameter, with Combined Diagnosis in red showing the highest sensitivity in both graphs.
Predictive performance of different variables.
Variables
AUC (95% CI)
Cutoff
Sensitivity
Specificity
Comparison with a combined diagnosis
Female
0.715 (0.615 ~ 0.802)
/
83.33%
59.70%
Z = 4.175, P < 0.001
Maximum lesion diameter (cm)
0.715 (0.615 ~ 0.802)
2.600
70.00%
70.15%
Z = 3.525, P < 0.001
Smoking
0.693 (0.591 ~ 0.783)
/
70.00%
68.66%
Z = 4.252, P < 0.001
f1 (×10−2)
0.819 (0.728 ~ 0.890)
71.42
66.67%
83.58%
Z = 2.252, P = 0.024
f2 (×10−2)
0.769 (0.672 ~ 0.848)
4.329
70.00%
76.12%
Z = 2.994, P = 0.003
f3 (×10−2)
0.780 (0.685 ~ 0.858)
8.166
96.67%
49.25%
Z = 2.758, P = 0.006
ADC (×10−3mm2/s)
0.742 (0.643 ~ 0.825)
1.167
63.33%
79.10%
Z = 3.194, P = 0.001
SUVmax (g/cm3)
0.767 (0.670 ~ 0.847)
9.860
56.67%
91.04%
Z = 2.960, P = 0.003
Combined Diagnosis
0.909 (0.833 ~ 0.958)
0.443
73.33%
89.55%
/
The combined diagnosis implies the combination of independent predictors and represents Smoking + f1 + ADC + SUVmax, CI, confidence interval.
Validation
Internal validation using bootstrap resampling confirmed the robustness of the composite model, yielding an AUC of 0.895 (95% CI: 0.875–0.906). The calibration curve and DCA plots demonstrated good calibration and clinical utility of the model for patients with NSCLC (Figure 4).
Calibration curve (a) and decision curve analysis (b) of the combination of independent predictors (smoking + f1 + SUVmax + ADC).
Panel (a) displays a calibration plot showing observed versus predicted risk with a red line indicating model prediction accuracy and a dashed line representing perfect calibration. Panel (b) presents a decision curve analysis with net benefit on the y-axis and threshold probability on the x-axis, showing three lines: red for the model, gray for treating all, and black for treating none.Discussion
Currently, 18F-FDG PET and DWI are widely used noninvasive imaging modalities for assessing glucose metabolism and water molecule diffusion, respectively. SUVmax, derived from 18F-FDG PET, reflects the peak glucose metabolism of tumors, while ADC, derived from DWI, quantifies the diffusion rate of water within tissues (21). Previous studies have reported that SCC has greater proliferative and invasive potential than AC, leading to distinct 18F-FDG metabolism levels and water diffusion rates. This makes SUVmax and ADC effective for distinguishing between the two subtypes (22, 23). In this study, SUVmax was significantly higher and ADC was lower in the SCC group than in the AC group, which is consistent with previous findings. Moreover, multivariate analysis confirmed that SUVmax and ADC are independent predictors, further supporting the utility of 18F-FDG PET and DWI in the assessment of NSCLC subtypes.
Building on the aforementioned research, this study innovatively introduced RSI into the differentiation of SCC from AC. RSI is an advanced diffusion imaging technique that assesses the movement of water molecules within human tissues (24). Unlike DWI, RSI does not assume a Gaussian distribution of water diffusion. Instead, it distinguishes and quantifies diffusion across multiple microstructural compartments (restricted, hindered, and free diffusion), allowing for a more precise evaluation of water movement (25). However, clinical studies on RSI have yielded inconsistent findings. For example, in breast cancer, both f1 and f3 can differentiate benign from malignant lesions, with malignant lesions exhibiting increased f1 and decreased f3 values (26). In contrast, in rectal cancer, only f1 can effectively distinguish high-grade from low-grade tumors, with higher f1 values observed in high-grade cases (27). In the present study, compared with the AC group, the SCC group exhibited lower f1 values and higher f2 and f3 values. Among these parameters, f1 was identified as an independent predictor for distinguishing SCC from AC. The parameters f1, f2, and f3 represent the proportions of restricted, hindered, and free diffusion, respectively, and their sum equals 1 (10). A possible explanation for the observed differences lies in the biological characteristics of the two tumor types. Although SCC is typically more invasive and has a higher cell density than AC (which would suggest an increase in the restricted diffusion compartment) (28, 29), the associated tissue microischemia and micronecrosis may expand the extracellular space and increase the free water content. This shift promotes greater hindered and free water diffusion. When the increase in restricted diffusion is outweighed by the increase in hindered and free diffusion, the f1 fraction decreases, leading to elevated f2 and f3 values (30). Additionally, differences in cell arrangement between SCC and AC may also contribute to this outcome.
Clinical factors, including age, sex distribution, smoking status, and maximum lesion diameter, were incorporated into the analysis. The results indicated that while sex distribution, smoking status, and maximum lesion diameter contributed to the differentiation of SCC from AC, only smoking status emerged as an independent predictor. This finding aligns with previous studies, reinforcing the role of smoking status as a simple and effective indicator for NSCLC subtyping (31). The underlying mechanism may be attributed to smoking-induced bronchial squamous epithelial carcinogenesis (32).
Despite these promising results, several limitations must be acknowledged. First, this was a single-center study with a relatively small sample size and an uneven distribution of tumor subtypes (30 cases of SCC vs. 67 cases of AC), which may affect the stability and generalizability of the predictive model. Second, research on RSI sequences—particularly in lung imaging—is still limited, and the optimal b-value for lesion evaluation has not yet been established. Third, MRI has limitations in detecting microscopic lesions. Fourth, despite the use of various techniques to mitigate respiratory and cardiovascular pulsation artifacts, their impact remains significant. Fifth, previous studies have suggested an association between tumour location and subtypes of NSCLC. Among the clinical factors considered in this paper, lesion location was not included as a potential influencing factor, which could have an adverse impact on the research results. Future research will focus on expanding sample sizes, conducting multicenter studies, reducing distribution disparities among different lesion subtypes, and incorporating additional clinical factors such as lesion location. Additionally, efforts will be made to optimize scanning protocols and improve image quality to ensure more stable and reliable results.
Conclusion
Smoking status, f1, SUVmax, and ADC are independent predictors for the differentiation of AC from SCC. The combination of these parameters shows potential as a biomarker for the classification of NSCLC subtypes.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by Henan Provincial People’s Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
We acknowledge the support received from Natural Science Foundation of Henan.
Conflict of interest
Authors YY and ZW were employed by the company United Imaging Healthcare.
The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
ReferencesSiegelRLGiaquintoANJemalA.
Cancer statistics, 2024. CA Cancer J Clin. (2024) 74:12–49. doi: 10.3322/caac.21820, PMID: 38230766HendriksLELRemonJFaivre-FinnCGarassinoMCHeymachJVKerrKM.
Non-small-cell lung cancer. Nat Rev Dis Primers. (2024) 10:71. doi: 10.1038/s41572-024-00551-9, PMID: 39327441RielyGJWoodDEEttingerDSAisnerDLAkerleyWBaumanJR.
Non-small cell lung cancer, version 4.2024, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. (2024) 22:249–74. doi: 10.6004/jnccn.2204.0023, PMID: 38754467LiuLSolerJReckampKLSankarK.
Emerging targets in non-small cell lung cancer. Int J Mol Sci. (2024) 25:10046. doi: 10.3390/ijms251810046, PMID: 39337530MiaoDZhaoJHanYZhouJLiXZhangT.
Management of locally advanced non-small cell lung cancer: State of the art and future directions. Cancer Commun (Lond). (2024) 44:23–46. doi: 10.1002/cac2.12505, PMID: 37985191StamatisG.
Staging of lung cancer: the role of noninvasive, minimally invasive and invasive techniques. Eur Respir J. (2015) 46:521–31. doi: 10.1183/09031936.00126714, PMID: 25976686HinzpeterRKulanthaiveluRKohanAMuradVMirshahvaladSAAveryL.
Predictive [18F]-FDG PET/CT-based radiogenomics modelling of driver gene mutations in non-small cell lung cancer. Acad Radiol. (2024) 31:5314–23. doi: 10.1016/j.acra.2024.06.038, PMID: 38997880OhnoYYuiMTakenakaDYoshikawaTKoyamaHKassaiY.
Computed DWI MRI results in superior capability for N-stage assessment of non-small cell lung cancer than that of actual DWI, STIR imaging, and FDG-PET/CT. J Magn Reson Imaging. (2023) 57:259–72. doi: 10.1002/jmri.28288, PMID: 35753082WhiteNSLeergaardTBD’ArceuilHBjaalieJGDaleAM.
Probing tissue microstructure with restriction spectrum imaging: Histological and theoretical validation. Hum Brain Mapp. (2013) 34:327–46. doi: 10.1002/hbm.21454, PMID: 23169482WhiteNSMcDonaldCFaridNKupermanJKarowDSchenker-AhmedNM.
Diffusion-weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging. Cancer Res. (2014) 74:4638–52. doi: 10.1158/0008-5472.CAN-13-3534, PMID: 25183788KrishnanAPKarunamuniRLeydenKMSeibertTMDelfantiRLKupermanJM.
Restriction spectrum imaging improves risk stratification in patients with glioblastoma. AJNR Am J Neuroradiol. (2017) 38:882–9. doi: 10.3174/ajnr.A5099, PMID: 28279985ZhangYYangCShengRDaiYZengM.
Preoperatively identify the microvascular invasion of hepatocellular carcinoma with the restricted spectrum imaging. Acad Radiol. (2023) 30:S30–9. doi: 10.1016/j.acra.2023.06.010, PMID: 37442719HeLQinYHuQLiuZZhangYAiT.
Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging. Breast Cancer Res. (2024) 26:71. doi: 10.1186/s13058-024-01828-3, PMID: 38658999LiuXMengNZhouYFuFYuanJWangZ.
Tri-compartmental restriction spectrum imaging based on 18F-FDG PET/MR for identification of primary benign and Malignant lung lesions. J Magn Reson Imaging. (2025) 61:830–40. doi: 10.1002/jmri.29438, PMID: 38886922ChenSGuYYuHChenXCaoTHuL.
NEMA NU2-2012 performance measurements of the United Imaging uPMR790: an integrated PET/MR system. Eur J Nucl Med Mol Imaging. (2021) 48:1726–35. doi: 10.1007/s00259-020-05135-9, PMID: 33388972LiuGCaoTHuLZhengJPangLHuP.
Validation of MR-based attenuation correction of a newly released whole-body simultaneous PET/MR system. BioMed Res Int. (2019) 2019:8213215. doi: 10.1155/2019/8213215, PMID: 31886254ConlinCCFengCHRodriguez-SotoAEKarunamuniRAKupermanJM.
Improved characterization of diffusion in normal and cancerous prostate tissue through optimization of multicompartmental signal models. J Magn Reson Imaging. (2021) 53:628–39. doi: 10.1002/jmri.27393, PMID: 33131186LoubrieSZouJRodriguez-SotoAELimJAndreassenMMSChengY.
Discrimination between benign and Malignant lesions with restriction spectrum imaging MRI in an enriched breast cancer screening cohort. J Magn Reson Imaging. (2025) 61:1876–87. doi: 10.1002/jmri.29599, PMID: 39291552Rami-PortaRNishimuraKKGirouxDJDetterbeckFCardilloGEdwardsJG.
The international association for the study of lung cancer lung cancer staging project: proposals for revision of the TNM stage groups in the forthcoming (Ninth) edition of the TNM classification for lung cancer. J Thorac Oncol. (2024) 19:1007–27. doi: 10.1016/j.jtho.2024.02.011, PMID: 38447919ShiehG.
Choosing the best index for the average score intraclass correlation coefficient. Behav Res Methods. (2016) 48:994–1003. doi: 10.3758/s13428-015-0623-y, PMID: 26182855DeiningerKKorfPLauberLGrimmRStreckerRSteinackerJ.
From phantoms to patients: improved fusion and voxel-wise analysis of diffusion-weighted imaging and FDG-positron emission tomography in positron emission tomography/magnetic resonance imaging for combined metabolic-diffusivity index (cDMI). Diagn (Basel). (2024) 14:1787. doi: 10.3390/diagnostics14161787, PMID: 39202275HuangZWangHTingFChenYFanHLiX.
Metabolic and multi-model intravoxel incoherent motion parameters based 18F-FDG PET/MRI for predicting subtypes of inoperable non-small cell lung cancer. BMC Cancer. (2025) 25:322. doi: 10.1186/s12885-025-13543-z, PMID: 39984874HuWDWangHCWangYBCuiLLChenXH.
Correlation study on 18F-FDG PET/CT metabolic characteristics of primary lesion with clinical stage in lung cancer. Q J Nucl Med Mol Imaging. (2021) 65:172–7. doi: 10.23736/S1824-4785.19.03146-7, PMID: 30916535YinHLiuWXueQSongCRenJLiZ.
The value of restriction spectrum imaging in predicting lymph node metastases in rectal cancer: a comparative study with diffusion-weighted imaging and diffusion kurtosis imaging. Insights Imaging. (2024) 15:302. doi: 10.1186/s13244-024-01852-z, PMID: 39699826BesasieBDSunnapwarAGGaoFTroyerDClarkeGDWhiteH.
Restriction spectrum imaging-magnetic resonance imaging to improve prostate cancer imaging in men on active surveillance. J Urol. (2021) 206:44–51. doi: 10.1097/JU.0000000000001692, PMID: 33617334QinYTangCHuQZhangYYiJDaiY.
Quantitative assessment of restriction spectrum MR imaging for the diagnosis of breast cancer and association with prognostic factors. J Magn Reson Imaging. (2023) 57:1832–41. doi: 10.1002/jmri.28468, PMID: 36205354XiongZGengZLianSYinSXuGZhangY.
Discriminating rectal cancer grades using restriction spectrum imaging. Abdom Radiol (NY). (2022) 47:2014–22. doi: 10.1007/s00261-022-03500-w, PMID: 35368206LiGHuangRZhuMDuMZhuJSunZ.
Native T1-mapping and diffusion-weighted imaging (DWI) can be used to identify lung cancer pathological types and their correlation with Ki-67 expression. J Thorac Dis. (2022) 14:443–54. doi: 10.21037/jtd-22-77, PMID: 35280462LiSLiuJZhangWLuHWangWLinL.
T1 mapping and multimodel diffusion-weighted imaging in the assessment of cervical cancer: a preliminary study. Br J Radiol. (2023) 96:20220952. doi: 10.1259/bjr.20220952, PMID: 37183908MengNLiuXZhouYYuXWuYFuF.
Multiparametric 18F-FDG PET/MRI based on restrictive spectrum imaging and amide proton transfer-weighted imaging facilitates the assessment of lymph node metastases in non-small cell lung cancer. Radiol Med. (2025) 130:1003–12. doi: 10.1007/s11547-025-01992-2, PMID: 40232656SabbulaBRGasalbertiDPMukkamallaSKRAnjumF.
Squamous cell lung cancer. In: StatPearls.
StatPearls Publishing, Treasure Island (FL (2024)., PMID: 33232091LiuBLiuYZouJZouMChengZ.
Smoking is Associated with Lung Adenocarcinoma and Lung Squamous Cell Carcinoma Progression through Inducing Distinguishing lncRNA Alterations in Different Genders. Anticancer Agents Med Chem. (2022) 22:1541–50. doi: 10.2174/1871520621666210727115147, PMID: 34315392
Edited by: Rong Niu, Third Affiliated Hospital of Soochow University, China
Reviewed by: Sandeep Kumar Mishra, Yale University, United States
Abir Swaidan, University of California, Los Angeles, United States